options(warn=-1)
library(tidyr)
library(dplyr)
## 
## Attaching package: 'dplyr'
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##     filter, lag
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library(plotly)
## Loading required package: ggplot2
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## Attaching package: 'plotly'
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library(rworldmap)
## Loading required package: sp
## ### Welcome to rworldmap ###
## For a short introduction type :   vignette('rworldmap')
library(maps)
library(ggmap)
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## Attaching package: 'ggmap'
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library(reshape2)
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library(raster)
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library(rgdal)
## rgdal: version: 1.2-8, (SVN revision 663)
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##  Loaded GDAL runtime: GDAL 2.1.3, released 2017/20/01
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##  Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.4/Resources/library/rgdal/proj
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library(rgeos)
## rgeos version: 0.3-23, (SVN revision 546)
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terr = read.csv('~/Documents/CodeWork/Global_Terrorism/globalterrorismdb_0616dist.csv', check.names = FALSE, header = TRUE, stringsAsFactors = FALSE)
terr=rename(terr, id=eventid, year=iyear, nation=country_txt, 
            Region=region_txt, attack=attacktype1_txt,
            target=targtype1_txt, weapon=weaptype1_txt, 
            Killed=nkill, wounded=nwound)

0.1 Data cleaning

We clean the data

terr$Killed=as.integer(terr$Killed)
terr$wounded=as.integer(terr$wounded)

terr$Killed[which(is.na(terr$Killed))] = 0
terr$wounded[which(is.na(terr$wounded))] = 0


terr$nation[terr$nation=="United States"] <- "USA"
terr$nation[terr$nation=="United Kingdom"] <- "UK"
terr$nation[terr$nation=="People's Republic of the Congo"] <- "Republic of Congo"
terr$nation[terr$nation=="Bosnia-Herzegovina"] <- "Bosnia and Herzegovina"
terr$nation[terr$nation=="Slovak Republic"] <- "Slovakia"
global_t <- 
  terr %>%
  group_by(year,nation,Region) %>%
  summarize(Total=n())

global_y <- global_t%>%group_by(year)%>%summarize(Total=sum(Total))
global_attacks <- 
  global_t %>%
  group_by(nation) %>%
  summarize(Total=sum(Total)) %>% 
  arrange(desc(Total))

attach(global_attacks)
global_n <- global_attacks[order(-Total),]
detach(global_attacks)

Let’s look at the number of terrorist attacks with the passage of time.

gy <- global_y %>%
  ggplot(mapping=aes(year,Total))+
  geom_line(color="red")+
  theme(legend.position="none", panel.background = NULL, axis.text.x = element_text(angle=45, vjust = 1))+
  labs(x="Year", y="Number of attacks", title="Number of global attacks over years")
ggplotly(gy, width = 800, height=480)
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
global_kills_years <- 
  terr %>%
  group_by(year) %>%
  summarize(killed=sum(Killed))
global_wound_years <- 
  terr %>%
  group_by(year) %>%
  summarize(wounded=sum(wounded))
globe <- 
  global_kills_years %>% 
  inner_join(global_wound_years, by="year")

df <- melt(globe, "year")
df=rename(df, effect=variable)

gky <- df %>%
  ggplot(mapping=aes(x=year,y=value, color=effect))+
  geom_line()+
  theme(panel.background = NULL, axis.text.x = element_text(angle=45, vjust = 1))+
  labs(x="Year", y="Count", title="Number of people killed/wounded over years")
ggplotly(gky, width = 800, height=450)
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

1 attacks by highest casualties(killed+wounded)

#get weapon most used in each nation
terr$casualties=as.integer(terr$Killed+terr$wounded)
terr$casualties[which(is.na(terr$casualties))]=0
g_max_cas <- terr%>%
  top_n(10, casualties) %>%
  ggplot(mapping=aes(x=reorder(target1, -casualties), y=casualties, fill=target1)) +
  geom_bar(stat = 'identity')+
  theme(legend.position="none", panel.background = NULL, axis.text.x =  element_text(angle=50, vjust = 1))+
  labs(x="Target of attack", y="Number of casulaties", title="Terrorist attacks with most casualties")
ggplotly(g_max_cas)
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`

Let’s look at the 40 countries with maximum number of terrorist attacks, and 40 countries with least number of terrorist attacks

g2 <- global_n%>%
  top_n(40) %>%
  ggplot(mapping=aes(x=reorder(nation, -Total),y=Total,fill=nation)) + 
  geom_bar(stat='identity')+
  theme(legend.position="none", panel.background = NULL, axis.text.x = element_text(angle=90, vjust = 1))+
  labs(x="Countries", y="Number of attacks", title="Countries with most number of terrorist attacks")
## Selecting by Total
ggplotly(g2, width = 800, height=450)
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
g2 <- global_n%>%
  top_n(-40)%>%
  ggplot(mapping=aes(x=reorder(nation, Total),y=Total,fill=nation)) + 
  geom_bar(stat='identity')+
  theme(legend.position="none", panel.background = NULL, axis.text.x = element_text(angle=90, vjust = 1))+
  labs(x="Countries", y="Number of attacks", title="Countries with least number of terrorist attacks")
## Selecting by Total
ggplotly(g2, width = 800, height=450)
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
g1 <- terr %>% 
  ggplot(aes(x = weapon, y = wounded, fill=weapon)) + 
  geom_boxplot() +
  theme(legend.position = "none",    axis.text.x =  element_text(angle=45))
ggplotly(g1, height = 500)
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`